Published: Apr 1, 2019

There Is a Range of Tasks Your Face Recognition App Can Be Designed to Perform If You Use the Right Face Recognition Methods. We’d Like to Share Our Experience

The Facial Recognition technology has been one of those, gaining ground fastest over recent years, and one that is still obviously pretty far from its heyday. Invented to virtually enhance, or rather, extend one of the 6 human senses, it is finding new, often, critically important (for example, public security-related) uses, becoming more wide-spread globally by the day.

According to, the total worth of the global Face Recognition software market is estimated to have constituted some USD 3.2 billion in 2019. It is also predicted to reach USD 7 billion in 2024, thus showing a 16.6% growth. This can only mean that while giving those better equipped with Face Recognition apps an edge and an additional means of control, the rapidly developing Facial Recognition technology is also becoming a competitive factor for businesses in various industry sectors. Simultaneously, the technology is also creating more challenges and opportunities for IT companies, engaged in developing Face Recognition applications.

In this article, we’ll dwell on the challenges we’ve encountered and dealt with while developing our Face Recognition system. We’ll also tell you about the various opportunities, associated with the Facial Recognition technology, and share several interesting insights we’ve gleaned. These insight can be of interest both to those looking to develop a Face Recognition app and to Face Recognition app developers.

So, get buckled up and here is our story…

The Task at Hand: Reliably Securing the Office Premises of a Mid-Sized Eastern-European IT Company

Facial Biometric identification

Our AI team was created when the rise of the AI technologies we have all witnessed over recent years had just begun. Face Recognition became a natural choice for us here: it was relatively easy to foresee many of the technology’s numerous forthcoming applications.

Although we’ve been involved in Face Recognition app development for close to two years now, our first internal project, focusing on the development of a Face Recognition system to secure access to our company premises, has become a major experimentation ground for our AI development team. This project is still helping us choose and test the recognition approaches that are more optimal for each of the various types of objects (including a human body, physical objects or facial mimics).

Our spacious office is located centrally in Downtown Kharkiv. Due to this, ensuring its security was not our primary goal. However, from the very outset, we wanted to acquire a consummate grasp of the Facial Recognition technology and opted for a full-blown, feature-rich and high-performance solution that would be on the cutting edge of technology. More specifically, the objectives of our Face Recognition app development endeavor can be summed up as follows:

  1. Finding out more precisely how the different Face Recognition algorithms work.

  2. Cutting down on our security-related costs.

  3. Obtaining office attendance stats.

  4. Entering a budding business niche and establishing a business presence in it.

Looking to Develop a Robust and Well-Performing Face Recognition App?

Our ultimate goal was developing a Face Recognition system that would not only identify faces unfailingly and give an alarm if a face is not recognized, but would also be integrated with a card system, block access to the company premises under certain circumstances, and, finally, serve as the only means of security in the longer run (statistically, a well-developed Face Recognition app proves to be, at least, 10% more “observant” than a human guard, who can easily get diverted or become less attentive because of weariness).

Although, we, currently, still have all the required physical security in place, we’ve envisaged two options here: a system that is intended to reinforce our physical security, and one that is capable of reliably securing the premises all on its own.

We’ve extensively used both Face Recognition (SVM, and, more specifically, the version with the multi-classification with probability) and Face Detection (HOG from Dlib) for achieving the objectives under the above goal. It took us quite a spell to gauge the efficiency of these tools and techniques in solving the different tasks that needed to be solved. Those interested in the technical details of our quest for the right solution can see some of us sifting through the mullock and whatever insights we’ve managed to dig up in the following table. Those, who are not interested in the technical details, can, probably, skip this one right to the conclusion we’ve made, presented here below.